Hi there! This week’s research recap highlights the most important investing insights and resources from the past seven days, complete with direct links to every source.
Alternative Assets
Music as an Asset Class (Stoikov, Singla, Cetin, Cendra Villalobos)
Analyzing 1,295 transactions from the Royalty Exchange, a marketplace for music royalties, the authors calibrate three discounted cash flow models to value music rights. The best-fitting model shows that “Life of Rights” assets earn 12.8% median annual net returns, comparable to the S&P 500’s 12.2% annualized. 10-year contracts yield 7.3%. With returns largely uncorrelated to equities, music royalties appear to be a promising but highly illiquid diversification asset for long-term investors.
Commodities
Understanding Gold (Erb and Harvey)
The authors show that gold has preserved purchasing power over millennia but offers near-zero real long-term returns. Its low correlation with stocks (≈ 0.1 over the past decade) makes it a valuable portfolio diversifier and partial crisis hedge, rising in 8 of 11 major stock market drawdowns. Recent price highs reflect financialization via ETFs, central-bank de-dollarization, and potential Basel III reforms that could classify gold as a Tier 1 asset. Overall, gold’s true value lies in diversification and systemic-risk insurance, not necessarily superior returns.
Gold and Bitcoin (Harvey)
This paper offers a detailed comparison between gold and Bitcoin. Both are scarce, decentralized, costly to mine, and nearly inflation-free. Yet gold, being tangible and durable, contrasts with Bitcoin’s digital fragility and high volatility. A 51% attack could erase Bitcoin’s value, while gold faces future supply risks from fusion or asteroid mining. The author concludes that both assets serve distinct roles as portfolio diversifiers.
Credit
RavenPack Quantitative Research: Introducing The RavenPack Credit News Factors (Hafez and Al Wakil)
A white paper from RavenPack introduces Credit News Factors, turning structured news into tradable signals for corporate bonds. Using 2015–2024 backtests, long–short portfolios built around analyst and credit rating changes, earnings, and price-target news achieved annualized returns of 3–13% and information ratios up to 1.3 (before costs), with the strongest results in high-yield bonds over 1–2-week horizons.
Crypto
Surprisingly Profitable Pre-Holiday Drift Signal for Bitcoin (Vojtko, Dujava, and Cmorej)
Bitcoin exhibits a clear pre-holiday drift, but only when paired with short-term momentum. Buying Bitcoin at the close before a holiday (D–1) when it hits a new 5-day high and selling after the holiday (D+1) yields annualized returns of 8.6%, volatility near 11%, and a Sharpe ratio of 0.79. Hence, combining calendar effects with momentum is a possible source of alpha in crypto markets.
Currencies
Assessing Cross-Currency Predictability in Forex Markets: Insights from Limit Order Book Data (Petrova, Vilhelmsson, and Nordén)
Can high-frequency limit order book data forecast short-term FX returns? Using one-minute to one-hour data on five major USD pairs, the authors test PCA, supervised PCA, LASSO, and random forests, finding no meaningful predictive power. Only brief one-minute order-flow signals show any edge. Today’s electronic FX markets thus exhibit strong efficiency, with only very brief inefficiencies.
RavenPack Quantitative Research: Enhancing Currency Risk Premia with RavenPack FX Sentiment Factors (Hafez, Al Wakil, Liu, and Bouchs)
A second RavenPack white paper shows that their FX sentiment factors, capturing both macroeconomic news trends and short-term media attention, enhance traditional carry, value, and momentum currency strategies when used as an overlay. Combining slow trend-following and fast mean-reverting signals lifts annual returns by up to 106 bps and raises information ratios to 0.39 (Carry), 0.51 (Value), 0.47 (Momentum), and 0.67 (Multi-Factor). Performance improvements persist across subsamples.
RavenPack Quantitative Research: Harnessing News Sentiment for FX Futures Strategies (Hafez, Al Wakil, Bouchs, and Sanchez)
A third white paper from RavenPack builds standalone FX trading strategies from its news sentiment data, showing that macroeconomic sentiment drives trend-following performance (IR ≈ 0.52), while FX-specific sentiment captures short-lived overreactions consistent with mean reversion (IR ≈ 0.63). Combining these complementary signals yields the strongest and most stable performance, with a composite information ratio near 0.62 (before costs) across holding periods.
Equities
Anomalies and Their Short-Sale Costs (Muravyev, Pearson, and Pollet)
Analyzing 162 equity anomalies from 2006–2020, the authors find that short-sale costs, not mispricing, explain most excess returns. The short legs drive performance before costs (0.14% per month), but once borrow costs are applied or high-fee stocks are removed, returns vanish or turn slightly negative. This pattern holds across microcaps and “strong” anomalies. Hence, most long-short anomalies disappear in practice due to stock borrow fees.
RavenPack Quantitative Research: Introducing Earnings Intelligence Factors (Hafez, Matas Navarro, Liu, and Gomez)
A fourth white paper from RavenPack introduces the “Earnings Intelligence Factors”, combining sentiment from earnings news, call transcripts, and insider trades into trading signals. Across 2007–2023, U.S. Small-cap portfolios achieved 33.4% annualized returns (IR = 2.8) and Mid/Large-caps 14.5% (IR = 1.9), with limited drawdowns even in 2008 and 2022. Results exclude trading costs. Thus, blending multiple earnings-related information sources can potentially deliver robust alpha across regions and horizons.
Machine Learning and Large Language Models
RavenPack Quantitative Research: Constructing a Machine Learning-Based Country Allocation Strategy Using News Sentiment (Hafez, Al Wakil, and Ammy-Driss)
A fifth white paper from RavenPack applies an elastic net logistic regression model to macroeconomic news sentiment for allocating across G10 equity markets. The approach achieves Information Ratios of 0.84 (2-day) and 0.61 (1-week) before costs, boosting annual returns by roughly 1–3% compared to naive sentiment averages.
All days are not created equal: Understanding momentum by learning to weight past returns (Beckmeyer and Wiedemann)
The authors train a neural network to learn how to weight past daily returns when forming momentum signals, rather than treating all days equally. It assigns the greatest weight to earnings announcements, market-wide jumps, and large price moves. The resulting strategy earns 18.5% annually (Sharpe 1.47; 0.77 after costs) versus 13.2% (0.49) for standard momentum, remains strong post-2003, and avoids crashes.
Financial Machine Learning: An Engineering Problem (Lopez de Prado)
Investing is an engineering, not a statistical, problem. Traditional econometric methods break down under the non-stationarity, noise, and small samples prevalent in finance, making causal inference essential. The author advocates engineering-based solutions, such as Hierarchical Risk Parity and other approaches, to build portfolios that are far more stable and resilient than those produced by Markowitz optimization.
Portfolio Management
How to Use the Sharpe Ratio (Lopez de Prado, Lipton, and Zoonekynd)
Most Sharpe ratios are misused because inference ignores skewed returns, short samples, and multiple testing. The authors introduce corrected measures that remain valid under non-normal returns and adjust for multiple testing, for example, the Deflated Sharpe Ratio, which accounts for the number and dispersion of backtests to measure how much of a high Sharpe may stem from data mining.
Blogs
Is the degradation of trend following performance a cohort effect, instrument decay, or an environmental problem? (Rob Carver)
Book and Workshop Introduction: Generative AI for Trading & Asset Management (Ernest Chan)
Linearity in Momentum: A Smarter Trend Signal for Asset Allocation (QuantSeeker)
Podcasts
Brent Penfold - Can Pre-Historic Strategies Still Make 30%pa? (The Algorithmic Advantage)
Using Alternative Data in a Backtest (Line Your Own Pockets)
Morgan Housel: The Hidden Reason You May Never Feel Rich (Meb Faber)
Social Media / Industry Research
Bond Market Focus: Yield Curves and Mean Reverting Rate Expectations (AQR)
Size and Factor Performance (Dan Rasmussen, Verdad)
Book on Deep Learning in Quantitative Trading (Zhang and Zohren, Cambridge Elements)
Last Week’s Most Popular Links
The Information Content of The Implied Volatility Surface: How to More Efficiently Use Option Information to Predict Stock Returns? (Han, Liu, and Tang)
Long-Run Interest Rate Differentials and the Profitability of Currency Carry (Kaebi and Martins)
The reasons why maximum diversification is better than minimum risk, including in terms of risk (Torrente and Uberti)
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